Motivation: Indoor Air Exposure

• Black Carbon, PM2.5 and CO are major pollutant from indoor emissions
• Prime Source of indoor emission is Cooking - using Solid Biomass Fuel (SBF)
• SBF use is common in LMICs like India, particularly the rural households (80% population)
• Cooking Emission can be as high as 20 times of the background concentration
• Factors that effect the exposure to indoor pollutants
  • Fuel usage in cooking
  • Type of cooking setup (oven type and kitchen type)
  • Household ventilation
  • Food choices and cooking time
• Primary emission reduction can be achieved by adopting clean fuels such as LPG
• However, affordability and fuel-stacking remains a big challenge
• Community and Household level actionable measures can be a solution to reduce exposure
• Very limited BC monitoring data from rural communities

Black Carbon - Measurement Challanges

• BC is component of combustion derived particulate matter (PM)
• BC is a strong absorber of solar radiation, with 20 year GWP of 4470
• BC has also been identified as a toxic pollutant - can impact human organ growth
• Monitoring BC is a big challenge
  • No particular chemical definition
  • BC/EC/Soot measurement techniques
    • Aethalometer: Real-time Light absorption measurement at 880nm
      • Real-time analysis
      • Correction schemes have been thoroughly studied and developed
      • Multi-wavelength light absorption data facilitates Source Apportionment of BC.
      • Artifacts: Scattering by particles, Loading effects on filter
    • EC-OC Analyzer: Thermal-optical technique
      • Semi real-time analysis on sampled aerosol filter
      • OC quantification
    • Photo-Acoustic Measurement: Optical characterization
      • Suspended aerosol particle
      • Free from loading effect
      • Measures aerosol light scattering
    • Single Particle Soot Photometer (SP2): Laser Induced Incandescence
      • High accuracy, sensitivity (in Femtograms) and rich dataset
      • Size range: 200-1500 nm
      • particle sizing capability
  • However,
    • SP2, PAAS, EC-OC can be very expensive - not adoptable in certain situations
    • May not be suitable for field deployment with limited logistics (e.g. rural setup)
    • Aethalometers are widely used for regulatory grade BC measurement, ease of use.
    • Latest micro-Aethalometer can be a solution in field deployment

Source Apportionment from Aethalometer Data

• Aethalometer Measurement Process:
  • light attenuation -> absorption coefficient \(b_{abs}\) -> apply correction -> BC mass concentration
  • Source of uncertainty:
    • Loading correction scheme utilized
    • Fixed scattering correction
    • Use of fixed Mass Absorption Cross Section (MAC)
• Source Apportionment Process:
  • Majorly based on Absorption Angstrom Exponent (AAE): a quantity to measure the spectral dependence of light absorption by aerosols. \[ b_{abs} = k \times \lambda^{-\alpha} \]
  • From a multi-wavelength aethelomater \(\alpha\) can be derived from log-linear regression fit between \(b_{abs}\) and \(\lambda\) (wavelength)

Sample AAE Estimation

• Sandradewi 2008 -> Aethalometer Model
  • Two component based apportionment : Fossil Fuel (FF) and Biomass Burning (BB) \[ b_{abs}(\lambda) = b_{abs,FF}(\lambda) + b_{abs,BB}(\lambda) \]
    
  • Uses fixed \(\alpha\) values for FF (\(\alpha_{FF}\)) and BB (\(\alpha_{BB}\)) to segregate the light absorption component

Equations

Source Apportionment Equations

Campaign Photos

Pollution Monitoring in Indian Villages

• Rural Household emissions were characterized
• RAMP (non-calibrated PM2.5 and CO) and MA300 devices were used
• MA300
  • Micro-aethalometer: light weight and portable.
  • 5 wavelength measurement: 370, 470, 528, 625, 880
  • Sampling Flow Rate: 150 mL/min
  • Data Collection Frequency: 5 min
• 3 X MA300 were deployed in two villages for indoor and outdoor monitoring.
• Participant Survey conducted for fuel usage, cooking practices
• Participant’s Building structures were mapped

Location Details

Stations in Map (Not included)

Household and Village Details

• We considered one household from each village.
• These two households were set up for indoor monitoring in addition to outdoor monitoring in the village center.

Building Structure

Household Structure

Pollutant Summary Statisics

## # A tibble: 12 × 5
##    House            variable     n   mean      sd
##    <fct>            <fct>    <dbl>  <dbl>   <dbl>
##  1 Village1_Outdoor BC        2021   16.4    8.59
##  2 Village1_Indoor  BC        1934   16.3    6.71
##  3 Village2_Outdoor BC        3753   14.8    7.58
##  4 Village2_Indoor  BC        3223   14.1    7.23
##  5 Village1_Outdoor PM2.5     2021   61.4   30.8 
##  6 Village1_Indoor  PM2.5     1428   64.2   28.2 
##  7 Village2_Outdoor PM2.5     3139   59.8   30.6 
##  8 Village2_Indoor  PM2.5     2036   63.4   44.9 
##  9 Village1_Outdoor CO        2021  823.   360.  
## 10 Village1_Indoor  CO        1428 1229.  1531.  
## 11 Village2_Outdoor CO        3139  965.   354.  
## 12 Village2_Indoor  CO        2036 1690.  1863.

Note: PM2.5 and BC are in \(\mu g/m^3\); CO in ppb

• Summary stat does not reflect difference - proposed Hypothesis generating:

• Range and variability in household - ventilation

• BC is local not PM2.5 - we are more confident

Analysis of BC concentration

Making a new data set for analysis

HH_4 = HH_3 %>% 
  separate(House, into = c("Village","Position"), sep = "_", remove = FALSE) %>%
  select(House,Village, Position, date, dDate, hour, PM2.5, BC, BCbb, BCff, AAE_Blue_IR, DayType, DayPart, activity)

HH_4 %>% group_by(Village,activity) %>% 
  summarise(n = n_distinct(dDate))

## `summarise()` has grouped output by 'Village'. You can override using the
## `.groups` argument.

## # A tibble: 6 × 3
## # Groups:   Village [2]
##   Village  activity     n
##   <chr>    <fct>    <int>
## 1 Village1 MrngCook    16
## 2 Village1 EvngCook    17
## 3 Village1 NoCook      21
## 4 Village2 MrngCook    20
## 5 Village2 EvngCook    21
## 6 Village2 NoCook      26

hourlyBC = HH_4 %>% select(House, hour, activity, BC) %>% 
  group_by(hour, House, activity)%>% 
  summarise(meanBC = mean(BC, na.rm=TRUE)) %>% 
  pivot_wider(names_from = "House", values_from = "meanBC")

## `summarise()` has grouped output by 'hour', 'House'. You can override using the
## `.groups` argument.

my_cols <- c("#00AFBB", "#E7B800", "#FC4E07")  
pairs(hourlyBC[,3:6], pch = 19,  cex = 0.5,
      col = my_cols[hourlyBC$activity],
      lower.panel=NULL)

pairs.panels(hourlyBC[,3:6],
             method = "pearson", # correlation method
             hist.col = "#00AFBB",
             density = TRUE,  # show density plots
             ellipses = TRUE # show correlation ellipses
              )

HH_4 %>% group_by(Village, Position, hour) %>%
  select(hour, Village, Position, BC, activity) %>% 
  mutate(row = row_number()) %>% 
  pivot_wider(names_from = "Position", values_from = "BC") %>% 
  mutate(IO_BC = Indoor/Outdoor) %>% 
  group_by(hour, Village, activity) %>% 
  summarise(meanIO = mean(IO_BC, na.rm=TRUE)) %>% 
  ggplot(.,aes(x = hour, y = meanIO, group = Village, color = Village))+
  geom_point()

## `summarise()` has grouped output by 'hour', 'Village'. You can override using
## the `.groups` argument.

HH_4 %>% group_by(Village, Position, hour) %>% 
  get_summary_stats(c(BC,BCbb, BCff), type = "mean_sd") %>% 
  ggplot(.,aes(x = hour, y = mean, fill= variable))+
  geom_area()+
  facet_grid(Village~Position)

HH_4 %>% group_by(Village, Position, hour) %>% 
  get_summary_stats(c(BC,BCbb, BCff, PM2.5), type = "mean_sd") %>% 
  mutate(row = row_number()) %>% 
  pivot_wider(names_from = "Position", values_from = c("mean","sd"))%>% 
  ggplot(.,aes(x = hour, y = mean_Indoor, group = Village, color = Village))+
  geom_point()+
  facet_wrap(~variable, ncol = 1, scales = "free_y")

## Warning: Removed 192 rows containing missing values (`geom_point()`).

HH_4 %>% 
  timeVariation(.,pollutant = "BC", group = "Position")

HH_3 %>% 
  group_by(House) %>% 
  summarise(minBC = min(BC),
            maxBC = max(BC),
            avgBC = mean(BC),
            sdBC = sd(BC))

## # A tibble: 4 × 5
##   House            minBC maxBC avgBC  sdBC
##   <fct>            <dbl> <dbl> <dbl> <dbl>
## 1 Village1_Outdoor 0.824  38.2  16.4  8.59
## 2 Village1_Indoor  5.44   38.2  16.3  6.71
## 3 Village2_Outdoor 0.801  38.2  14.8  7.58
## 4 Village2_Indoor  3.58   38.2  14.1  7.23

HH_3 %>% 
  ggplot(.,aes(x = BC))+
  geom_density()+
  facet_grid(House~activity)

HH_3 %>% 
  ggplot(.,aes(x = date, y = BC))+
  geom_point()

HH_3 %>% 
  filter(BC<1) %>% 
  ggplot(.,aes(x = BC))+
  geom_histogram()+
  facet_grid(House~DayPart)

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

HH_3 %>% filter(BC>0.2) %>% 
  ggplot(.,aes(y = House, x = BC))+
  geom_boxplot()+
  facet_grid(~activity)

HH_3 %>% filter(BC>0.3) %>% 
  group_by(House) %>% 
  summarise(n = n(), 
            day = n_distinct(dDate))

## # A tibble: 4 × 3
##   House                n   day
##   <fct>            <int> <int>
## 1 Village1_Outdoor  2021    11
## 2 Village1_Indoor   1934    12
## 3 Village2_Outdoor  3753    18
## 4 Village2_Indoor   3223    20

HH_3 %>% filter(BC<0.4) %>% 
  ggplot(.,aes(x = date, y = T))+
  geom_point()

HH_3 %>% filter(BC>0.4) %>% 
  select(hour, activity, House, BC) %>% 
  mutate(row = row_number()) %>% 
  separate(House, into = c("Village","Position"), sep = "_") %>%
  pivot_wider(names_from = "Position", values_from = "BC") %>% 
  group_by(Village, hour, activity) %>% 
  summarise(meanIN = mean(Indoor, na.rm=TRUE),
            meanOUT = mean(Outdoor, na.rm=TRUE)) %>% 
  ggplot(.,aes(x = meanIN, y = meanOUT, group = Village, color = activity))+
  geom_point()+
  facet_grid(~Village)

## `summarise()` has grouped output by 'Village', 'hour'. You can override using
## the `.groups` argument.

BC concentration by activity period

activity	meanBC	meanBCbb	meanBCff	pctChBC	pctChBCbb	pctChBCff
MrngCook	18.04	16.99	1.05	35.44	35.44	35.44
EvngCook	20.51	19.31	1.20	53.97	53.97	53.97

Notes: * percentage change in all BC components are same!

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

## `summarise()` has grouped output by 'activity'. You can override using the
## `.groups` argument.

HH_3 %>% ggplot(.,aes(x = DayPart, y = BCff, fill = House))+
  geom_boxplot(position=position_dodge(0.8))+
  theme_pubr()

Diurnal Variation

Reference measurement vs RAMP data

Spatial Data